{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13bba6ab-1b18-4a7f-b4ce-aca1a7af21c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "葡萄酒数据集如下:\n",
      "     label     a1    a2    a3    a4   a5    a6    a7    a8    a9    a10   a11  \\\n",
      "0        1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
      "1        1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
      "2        1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
      "3        1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
      "4        1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
      "..     ...    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
      "173      3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
      "174      3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
      "175      3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
      "176      3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
      "177      3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
      "\n",
      "      a12   a13  \n",
      "0    3.92  1065  \n",
      "1    3.40  1050  \n",
      "2    3.17  1185  \n",
      "3    3.45  1480  \n",
      "4    2.93   735  \n",
      "..    ...   ...  \n",
      "173  1.74   740  \n",
      "174  1.56   750  \n",
      "175  1.56   835  \n",
      "176  1.62   840  \n",
      "177  1.60   560  \n",
      "\n",
      "[178 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv(\"wine.data\",names=names)\n",
    "print(\"葡萄酒数据集如下:\")\n",
    "print(dataset)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3456a7d-73a5-4e2c-84d7-846018683aec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1 中异常值: []\n",
      "a 2 中异常值: [5.8 5.51 5.65]\n",
      "a 3 中异常值: [1.36 3.22 3.23]\n",
      "a 4 中异常值: [10.6 30.0 28.5 28.5]\n",
      "a 5 中异常值: [151.0 139.0 136.0 162.0]\n",
      "a 6 中异常值: []\n",
      "a 7 中异常值: []\n",
      "a 8 中异常值: []\n",
      "a 9 中异常值: [3.28 3.58]\n",
      "a 10 中异常值: [10.8 13.0 11.75 10.68]\n",
      "a 11 中异常值: [1.71]\n",
      "a 12 中异常值: []\n",
      "a 13 中异常值: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('seaborn-darkgrid')\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "data.plot(kind='box',subplots=True,layout=(3,5),sharex=False,sharey=False)\n",
    "p=data.boxplot(return_type='dict')\n",
    "for i in range(13):\n",
    "    y=p['fliers'][i].get_ydata()\n",
    "    print('a',i+1,'中异常值:',y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bef6a92e-bd49-4825-a6bf-2d9c12ea7390",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51861254 -0.56906261  0.26105088 ...  0.39346131  1.83381234\n",
      "   1.01300893]\n",
      " [ 0.24628963 -0.50234086 -0.90869274 ...  0.43875109  1.10735109\n",
      "   0.96524152]\n",
      " [ 0.19687903  0.05049647  1.22911457 ...  0.34817153  0.7860317\n",
      "   1.39514818]\n",
      " ...\n",
      " [ 0.33275817  1.88057869 -0.4246609  ... -1.64457872 -1.46320409\n",
      "   0.28057537]\n",
      " [ 0.20923168  0.26972507  0.01903496 ... -1.59928894 -1.37938164\n",
      "   0.29649784]\n",
      " [ 1.39508604  1.70900848  1.51146647 ... -1.55399916 -1.40732245\n",
      "  -0.59516041]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv(\"wine-clean.data\",names=names)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)].values.reshape(1,178)[0]\n",
    "cdata=preprocessing.StandardScaler().fit_transform(data)\n",
    "print(cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14255c61-b17f-4d2f-89a9-3507f6b909c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "x,y=cdata,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1,test_size=50)\n",
    "k_range=range(1,15)\n",
    "k_error=[]\n",
    "for k in k_range:\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "    scores=cross_val_score(model,x,y,cv=5,scoring='accuracy')\n",
    "    k_error.append(1-scores.mean())\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.plot(k_range,k_error,'r-')\n",
    "plt.xlabel('k的取值')\n",
    "plt.ylabel('预测误差率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75b17427-f8ee-4f77-9401-24336182b0e5",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2511396419.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[15], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m    model=KNeighborsClassifier(n=neighbors=9)\u001b[0m\n\u001b[1;37m                                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "model=KNeighborsClassifier(n=neighbors=9)\n",
    "model.fit(x_train,y_train)\n",
    "pred=model.predict(x_test)\n",
    "ac=accuracy_score(y_test,pred)\n",
    "print(\"模型预测准确率:\",ac)\n",
    "print(\"测试集的预测标签:\",pred)\n",
    "print(\"测试集的真实标签:\",y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "331e56ce-cfcc-4714-9de5-4b66692e640e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
